In practice, a number of other factors,
related to training data and network design,
may also obstruct the theoretical ideal
of perfect translation invariance [1]}.
While humans can typically recognise an object anywhere
in their visual field after seeing it in a single position,
the networks applied to image recognition tasks
frequently lack such flexible generalisation abilities
[2]}.
Nonetheless, a number of computational neuroscientists
see appropriately trained convolutional architectures
as having a close resemblance to the primate visual system
[3]}, [4]}, [5]}.
